636 lines
26 KiB
Python
636 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch Clvp model. """
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import gc
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import tempfile
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import unittest
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import datasets
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import numpy as np
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from transformers import ClvpConfig, ClvpDecoderConfig, ClvpEncoderConfig
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from transformers.testing_utils import (
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require_torch,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import ClvpEncoder, ClvpForCausalLM, ClvpModel, ClvpModelForConditionalGeneration
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from transformers import ClvpFeatureExtractor, ClvpTokenizer
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class ClvpEncoderTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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seq_length=7,
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is_training=False,
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use_input_mask=True,
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use_labels=True,
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vocab_size=50,
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hidden_size=128,
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projection_dim=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=32,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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def get_config(self):
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encoder_config = ClvpEncoderConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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)
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return encoder_config
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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encoder_config = self.get_config()
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return encoder_config, input_ids, input_mask
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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speech_config, input_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids.to(torch_device), "attention_mask": input_mask.to(torch_device)}
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return speech_config, inputs_dict
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def create_and_check_model(self, speech_config, input_ids, input_mask):
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text_config = ClvpEncoderConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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text_encoder_model = ClvpEncoder(config=text_config)
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text_encoder_model.to(torch_device)
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text_encoder_model.eval()
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with torch.no_grad():
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result = text_encoder_model(input_ids, attention_mask=input_mask)
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result = text_encoder_model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.projection_dim))
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# now check with speech config
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speech_encoder_model = ClvpEncoder(config=speech_config)
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speech_encoder_model.to(torch_device)
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speech_encoder_model.eval()
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with torch.no_grad():
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result = speech_encoder_model(input_ids, attention_mask=input_mask)
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result = speech_encoder_model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.projection_dim))
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@require_torch
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class ClvpEncoderTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (ClvpEncoder,) if is_torch_available() else ()
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test_pruning = False
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test_head_masking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = ClvpEncoderTester(self)
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self.encoder_config_tester = ConfigTester(self, config_class=ClvpEncoderConfig, hidden_size=32)
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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torch.cuda.empty_cache()
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def test_config(self):
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self.encoder_config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip(reason="ClvpEncoder does not output loss")
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def test_training(self):
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pass
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@unittest.skip(reason="ClvpEncoder does not output loss")
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def test_training_gradient_checkpointing(self):
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pass
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class ClvpDecoderTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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seq_length=3,
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is_training=False,
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vocab_size=300,
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max_position_embeddings=256,
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max_text_tokens=256,
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use_input_mask=True,
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hidden_size=128,
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num_hidden_layers=2,
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num_attention_heads=2,
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bos_token_id=97,
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eos_token_id=98,
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relative_attention_num_buckets=4,
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relative_attention_max_distance=16,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.max_text_tokens = max_text_tokens
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self.use_input_mask = use_input_mask
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_hidden_layers
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.relative_attention_max_distance = relative_attention_max_distance
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def get_config(self):
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decoder_config = ClvpDecoderConfig(
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vocab_size=self.vocab_size,
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max_position_embeddings=self.max_position_embeddings,
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max_text_tokens=self.max_text_tokens,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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relative_attention_max_distance=self.relative_attention_max_distance,
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)
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return decoder_config
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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decoder_config = self.get_config()
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return decoder_config, input_ids, input_mask
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def create_and_check_model(self, config, input_ids, attention_mask):
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model = ClvpForCausalLM(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids=input_ids, attention_mask=attention_mask)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.vocab_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids.to(torch_device),
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"attention_mask": attention_mask.to(torch_device),
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}
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return config, inputs_dict
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@require_torch
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class ClvpDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (ClvpModel, ClvpForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (ClvpForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": ClvpModelForConditionalGeneration} if is_torch_available() else {}
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test_pruning = False
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def setUp(self):
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self.model_tester = ClvpDecoderTester(self)
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self.decoder_config_tester = ConfigTester(self, config_class=ClvpDecoderConfig, hidden_size=32)
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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torch.cuda.empty_cache()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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if return_labels and model_class == ClvpForCausalLM:
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inputs_dict["labels"] = torch.zeros(
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[self.model_tester.batch_size, self.model_tester.seq_length], device=torch_device
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).long()
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return inputs_dict
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def test_training(self):
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# we will only test the ClvpForCausalLM since it outputs loss
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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model = ClvpForCausalLM(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, ClvpForCausalLM, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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# we will only test the ClvpForCausalLM since it outputs loss
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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model = ClvpForCausalLM(config)
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model.to(torch_device)
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, ClvpForCausalLM, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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class ClvpModelForConditionalGenerationTester:
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def __init__(self, parent, is_training=False):
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self.parent = parent
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self.clvp_encoder_tester = ClvpEncoderTester(parent)
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self.is_training = is_training
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self.batch_size = self.clvp_encoder_tester.batch_size # need bs for batching_equivalence test
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def get_config(self):
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decoder_config = ClvpDecoderConfig(
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vocab_size=50,
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max_position_embeddings=30,
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max_text_tokens=30,
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hidden_size=128,
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num_hidden_layers=1,
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num_attention_heads=2,
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bos_token_id=97,
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eos_token_id=98,
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relative_attention_num_buckets=4,
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relative_attention_max_distance=16,
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)
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text_config = self.clvp_encoder_tester.get_config()
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speech_config = self.clvp_encoder_tester.get_config()
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speech_config.vocab_size = 300
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return ClvpConfig.from_sub_model_configs(
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text_config,
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speech_config,
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decoder_config,
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projection_dim=16,
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)
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def prepare_config_and_inputs(self):
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_, input_ids, attention_mask = self.clvp_encoder_tester.prepare_config_and_inputs()
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ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
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_, audio, sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
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feature_extractor = ClvpFeatureExtractor()
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input_features = feature_extractor(raw_speech=audio, sampling_rate=sr, return_tensors="pt")[
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"input_features"
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].to(torch_device)
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config = self.get_config()
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return config, input_ids, attention_mask, input_features
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def create_and_check_model(self, config, input_ids, attention_mask, input_features):
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model = ClvpModelForConditionalGeneration(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids=input_ids, input_features=input_features, attention_mask=attention_mask)
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self.parent.assertEqual(result.logits_per_speech.shape, (2, self.clvp_encoder_tester.batch_size))
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self.parent.assertEqual(result.logits_per_text.shape, (self.clvp_encoder_tester.batch_size, 2))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask, input_features = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids.to(torch_device),
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"attention_mask": attention_mask.to(torch_device),
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"input_features": input_features.to(torch_device),
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"return_loss": False,
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}
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return config, inputs_dict
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@require_torch
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class ClvpModelForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (ClvpModelForConditionalGeneration,) if is_torch_available() else ()
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = ClvpModelForConditionalGenerationTester(self)
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self.clvp_config_tester = ConfigTester(self, config_class=ClvpConfig, hidden_size=32)
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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torch.cuda.empty_cache()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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# check for decoder model, text encoder model and speech encoder model hidden states
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decoder_hidden_states = outputs.decoder_hidden_states
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text_encoder_hidden_states = outputs.text_encoder_hidden_states
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speech_encoder_hidden_states = outputs.speech_encoder_hidden_states
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# check length of the hidden states
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expected_decoder_num_layers = config.decoder_config.num_hidden_layers + 1
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self.assertEqual(len(decoder_hidden_states), expected_decoder_num_layers)
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expected_speech_encoder_num_layers = config.text_config.num_hidden_layers + 1
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self.assertEqual(len(text_encoder_hidden_states), expected_speech_encoder_num_layers)
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expected_text_encoder_num_layers = config.speech_config.num_hidden_layers + 1
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self.assertEqual(len(speech_encoder_hidden_states), expected_text_encoder_num_layers)
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# check shapes of each hidden state
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# for the decoder model we will only test the dimension because the ClvpConditioningEncoder could increase
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# the sequence lengths.
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self.assertEqual(decoder_hidden_states[0].shape[-1], config.decoder_config.hidden_size)
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# the testing for text encoder stays standard because we just pass the text tokens here.
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self.assertListEqual(
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list(text_encoder_hidden_states[0].shape[-2:]),
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|
[self.model_tester.clvp_encoder_tester.seq_length, config.text_config.hidden_size],
|
|
)
|
|
|
|
# for the decoder model we will only test the dimension because the fix_decoder_outputs method could increase
|
|
# the sequence lengths by adding `decoder_fixing_codes` tokens at the end.
|
|
self.assertEqual(speech_encoder_hidden_states[0].shape[-1], config.speech_config.hidden_size)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="ClvpModelForConditionalGeneration does not have get_input_embeddings")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="ClvpModelForConditionalGeneration does not have get_input_embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
# override as the `logit_scale` parameter initilization is different for Clvp
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
# check if `logit_scale` is initilized as per the original implementation
|
|
if name == "logit_scale":
|
|
expected_value = np.log(1 / 0.07)
|
|
returned_value = param.data.item()
|
|
|
|
self.assertAlmostEqual(
|
|
returned_value,
|
|
expected_value,
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
expected_range = [0.0, 1.0]
|
|
returned_range = ((param.data.mean() * 1e9).round() / 1e9).item()
|
|
|
|
self.assertIn(
|
|
returned_range,
|
|
expected_range,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
def test_load_speech_text_decoder_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save ClvpConfig and check if we can load ClvpEncoderConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
encoder_config = ClvpEncoderConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), encoder_config.to_dict())
|
|
|
|
# Save ClvpConfig and check if we can load ClvpDecoderConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
decoder_config = ClvpDecoderConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.decoder_config.to_dict(), decoder_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "susnato/clvp_dev"
|
|
model = ClvpModelForConditionalGeneration.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# Since Clvp has a lot of different models connected with each other it's better to test each of them individually along
|
|
# with a test_full_model_integration. If the model breaks in future, it could be of a great help to identify the broken part.
|
|
|
|
|
|
@slow
|
|
@require_torch
|
|
class ClvpIntegrationTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.text = "This is an example text."
|
|
ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
|
|
_, self.speech_samples, self.sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
|
|
|
|
self.model = ClvpModelForConditionalGeneration.from_pretrained("susnato/clvp_dev").to(torch_device)
|
|
self.model.eval()
|
|
tokenizer = ClvpTokenizer.from_pretrained("susnato/clvp_dev")
|
|
feature_extractor = ClvpFeatureExtractor.from_pretrained("susnato/clvp_dev")
|
|
|
|
tokenizer_output = tokenizer(self.text, return_tensors="pt")
|
|
self.text_tokens = tokenizer_output["input_ids"].to(torch_device)
|
|
self.input_features = feature_extractor(
|
|
raw_speech=self.speech_samples, sampling_rate=self.sr, return_tensors="pt"
|
|
)["input_features"].to(torch_device)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
# clean-up as much as possible GPU memory occupied by PyTorch
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_conditional_encoder(self):
|
|
with torch.no_grad():
|
|
conditioning_encoder_outputs = self.model.conditioning_encoder(
|
|
input_features=self.input_features, input_ids=self.text_tokens
|
|
).to("cpu")
|
|
|
|
self.assertEqual(
|
|
conditioning_encoder_outputs.shape,
|
|
torch.Size((self.input_features.shape[0], 18, self.model.config.decoder_config.hidden_size)),
|
|
)
|
|
|
|
EXPECTED_OUTPUTS = torch.tensor(
|
|
[[-0.8582, 0.5228, 1.9944], [-0.0465, -1.1017, -0.0093], [-0.0466, -0.6030, -0.1280]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(conditioning_encoder_outputs[0, :3, :3], EXPECTED_OUTPUTS, atol=1e-4))
|
|
|
|
def test_decoder_model_generate(self):
|
|
autoregressive_model_output = self.model.speech_decoder_model.generate(input_ids=self.text_tokens).cpu()
|
|
|
|
EXPECTED_OUTPUTS = torch.tensor([[147, 2, 54, 2, 43, 2, 169, 122, 29, 64, 2, 136, 37, 33, 9, 8193]])
|
|
|
|
self.assertTrue(torch.allclose(autoregressive_model_output, EXPECTED_OUTPUTS))
|
|
|
|
def test_text_and_speech_encoder_models(self):
|
|
# check for text embeds
|
|
text_embeds = self.model.text_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
|
|
|
|
# fmt: off
|
|
EXPECTED_TEXT_EMBEDS = torch.tensor([1.4798, -2.0005, 2.3902, -0.5042, 1.6401, -2.4135, -1.4800, 3.0118, -2.4422, 1.3266, 2.2339, 1.4761, -4.8983, -1.3592, 6.0251, 6.7364, 2.2576, 3.7229, -10.0436, 4.6676])
|
|
# fmt: on
|
|
|
|
self.assertTrue(torch.allclose(text_embeds[0, :20], EXPECTED_TEXT_EMBEDS, atol=1e-4))
|
|
|
|
# check for speech embeds
|
|
speech_embeds = self.model.speech_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
|
|
|
|
# fmt: off
|
|
EXPECTED_SPEECH_EMBEDS = torch.tensor([3.1202, -3.1183, -1.4264, -6.1339, 1.8885, -0.1983, 0.9461, -1.7414, 0.3320, -3.8400, -1.5715, 1.5096, -1.7576, 0.2387, 4.9758, 5.8450, -6.2534, 2.8587, -5.5816, 4.7821])
|
|
# fmt: on
|
|
|
|
self.assertTrue(torch.allclose(speech_embeds[0, :20], EXPECTED_SPEECH_EMBEDS, atol=1e-4))
|
|
|
|
def test_full_model_integration(self):
|
|
full_model_output = self.model.generate(
|
|
input_ids=self.text_tokens,
|
|
input_features=self.input_features,
|
|
do_sample=False,
|
|
num_beams=4,
|
|
num_return_sequences=4,
|
|
max_new_tokens=10,
|
|
)
|
|
|
|
EXPECTED_SPEECH_IDS = torch.tensor([[1953, 1080, 612], [1953, 612, 493], [1953, 612, 716]])
|
|
EXPECTED_SIMILARITY_SCORES = torch.tensor([[14.7660, 14.4569, 13.6472, 13.5683]])
|
|
|
|
self.assertTrue(torch.allclose(full_model_output.speech_ids.cpu()[-3:, -3:], EXPECTED_SPEECH_IDS))
|
|
self.assertTrue(torch.allclose(full_model_output.logits_per_text.cpu(), EXPECTED_SIMILARITY_SCORES))
|